Update app.py

app.py

@@ -424,30 +424,32 @@ if st.button("🚀 Run Topic Modeling Pipeline", type="primary", use_container_w
         else:
             ax4.text(0.5, 0.5, "Theme not found", ha='center', transform=ax4.transAxes)
 
-        # --------------------------------------------------
-        # 5. KPI Dashboard
+# --------------------------------------------------
+        # 5. KPI Dashboard (Updated with UMass)
         # --------------------------------------------------
         ax5 = plt.subplot(3, 2, 5)
         ax5.axis('off') 
-        ax5.set_title("STEP 5: Post-Hoc Evaluation Formulas", fontsize=13, fontweight='bold', y=0.9)
+        ax5.set_title("STEP 5: Post-Hoc Evaluation Formulas", fontsize=13, fontweight='bold', y=0.95)
         
-        # Get the actual distance metric used for the label
         dist_used = ui_metric if algo_used == "UMAP" else "euclidean"
         
+        # Condensed to fit all 4 metrics beautifully!
         kpi_math = (
-            r"$\mathbf{Topic \ Diversity:} \quad D = \frac{| \text{Unique Words} |}{| \text{Total Words} |}$" + "\n"
-            f"Live Calc: {u_words_len} Unique / {t_words_len} Total = {div_status}\n\n"
+            r"$\mathbf{Diversity:} \quad D = \frac{| \text{Unique} |}{| \text{Total} |}$" + f"  [Live: {div_status}]\n\n"
+            
+            r"$\mathbf{Silhouette:} \quad S = \frac{b - a}{\max(a, b)}$" + f"  [Live: {sil_status}]\n\n"
             
-            r"$\mathbf{Silhouette \ Score:} \quad S = \frac{b - a}{\max(a, b)}$" + "\n"
-            f"Live Calc: Score = {sil_status} (Using {dist_used} distance)\n\n"
+            r"$\mathbf{NPMI:} \quad \frac{\log(P(x,y) / P(x)P(y))}{-\log P(x,y)}$" + f"  [Live: {npmi_status}]\n\n"
             
-            r"$\mathbf{NPMI \ Coherence:} \quad \text{NPMI}(x, y) = \frac{\log \frac{P(x,y)}{P(x)P(y)}}{-\log P(x,y)}$" + "\n"
-            f"Live Calc: Score = {npmi_status} across {len(all_words)} valid themes"
+            r"$\mathbf{UMass:} \quad \log \frac{P(x,y) + \epsilon}{P(x)}$" + f"  [Live: {umass_status}]"
         )
         
-        ax5.text(0.5, 0.4, kpi_math, fontsize=12, va='center', ha='center', 
-                 bbox=dict(boxstyle="square,pad=1.5", facecolor='#e6f2ff', edgecolor='#377eb8', lw=2))
-
+        ax5.text(0.5, 0.45, kpi_math, fontsize=12, va='center', ha='center', 
+                 bbox=dict(boxstyle="square,pad=1.2", facecolor='#e6f2ff', edgecolor='#377eb8', lw=2))
+        
+        ax5.text(0.5, -0.15, "Math: UMass measures internal dataset logic. NPMI measures external logic.\nSilhouette measures geometric separation.", 
+                 fontsize=10, ha='center', va='top', transform=ax5.transAxes, bbox=box_style)
+        
         # --------------------------------------------------
         # 6. Summary Matrix Transformations
         # --------------------------------------------------